Crop recommendation system for precision agriculture

Pudumalar, S.; Ramanujam, E.; Rajashree, R. Harine; Kavya, C.; Kiruthika, T.; Nisha, J Rahamath

doi:10.1109/icoac.2017.7951740

Cited by 203 publications

(60 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The System uses majority voting technique. Random tree, CHAID, K-Nearest Neighbor and Naive Bayes techniques are used to provide the recommendations [17].…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Technique for Crop Recommendation in Agriculture Sector

Patil,

Saiyyad

2019

IJEAT

View full text Add to dashboard Cite

Numerous efforts have been demonstrated through various innovations to lead towards the betterment of agriculture sector till now. Effective and innovative use of Science and Technology can help to improve crop quality and production, yield prediction and crop disease analysis. Agriculture sector provides various productions such as food, raw material for industry and has a significant impact on economy and employment of a country. The agriculture sector contains huge data with respect to factors affecting its input and output. With advances in technology, various data mining techniques have been introduced accordingly. Also different analytical models like Decision Tree, Random Forest, Support Vector Machine and Bayesian Neural Network are available for required analysis. These data mining techniques can be used to analyze the multidimensional, time specific data of agriculture sector to produce effective knowledge in support of the efforts to boost the economy. These methods can be further used to analyze soil, climate, moisture, humidity, temperature. In this paper, we used Naive Bayesian Classification technique to identify the class of crop and used the food grain dataset to analyze the technique over different attribute. Further we used Machine Learning approach for the accurate crop recommendation. Crop recommendation will help for effective decision making to the end user to take proper decision according to the output of the system.

show abstract

“…The System uses majority voting technique. Random tree, CHAID, K-Nearest Neighbor and Naive Bayes techniques are used to provide the recommendations [17].…”

Section: Literature Surveymentioning

confidence: 99%

Machine Learning Technique for Crop Recommendation in Agriculture Sector

Patil,

Saiyyad

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…The crop recommendation system using a support vector machine and artificial neural networks and the rules induced from the ensemble approach (support vector machine, naive Bayes, multilayer perceptron, and random forest algorithm) based on the soil is implemented by Rohit Kumar Rajak et.al [18]. Crop Recommendation system using an ensemble approach is implemented by S.Pudumalar et.al [19] by considering only soil type using a random tree, k-nearest neighbor and naive Bayes are combined as an ensemble approach. The design and implementation of crop and fertilizer recommendation systems are discussed by Hao Zhang et.al [20] considering meteorological data, soil data, and crop data.…”

Section: Literature Reviewmentioning

confidence: 99%

The Design of Hybrid Crop Recommendation System using Machine Learning Algorithms

Viviliya*¹,

V²

2019

IJITEE

View full text Add to dashboard Cite

Crop Recommendation System for agriculture is based on various input parameters. This paper proposes a hybrid model for recommending crops to south Indian states by considering various attributes. The recommender model is built as a hybrid model using the classifier algorithm such as Naive Bayes, J48 and association rules. Based on the appropriate parameters, the system will recommend the crop. Technologybased crop recommendation system for agriculture helps the farmers to increase the crop yield by recommending a suitable crop for their land with the help of geographic and the climatic parameters. The proposed hybrid recommender model is found to be effective in recommending a suitable crop

show abstract

“…Authors in [2] conclude that the nations planned guiding principle made in agriculture development field has a need for crop yield prediction to help farmers reduce chemical use in crop production and prevent soil degradation while still ensuring increased productivity in agriculture and efficient use of water resources. The data mining techniques would help farmers to select the right seeds to sow based on soil requirements and ensuring increased productivity to achieve profit of such technique [3]. Using the majority voting techniques as Random tree, CHAID ,K-Nearest Neighbor and Naïve Bayes algorithms to build a ensemble recommendation model to propose a crop based on site specific parameters accurately and efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%

Crop Recommendation using Machine Learning Techniques

2019

IJITEE

View full text Add to dashboard Cite

Precision agriculture (PA) allows precise utilization of inputs like seed, water, pesticides, and fertilizers at the right time to the crop for maximizing productivity, quality and yields. By deploying sensors and mapping fields, farmers can understand their field in a better way conserve the resources being used and reduce adverse affects on the environment. Most of the Indian farmers practice traditional farming patterns to decide crop to be cultivated in a field. However, the farmers do not perceive crop yield is interdependent on soil characteristics and climatic condition. Thus this paper proposes a crop recommendation system which helps farmers to decide the right crop to sow in their field. Machine learning techniques provide efficient framework for data-driven decision making. This paper provides a review on set of machine learning techniques to support the farmers in making decision about right crop to grow depending on their field’s prominent attributes.

show abstract

Crop recommendation system for precision agriculture

Cited by 203 publications

References 10 publications

Machine Learning Technique for Crop Recommendation in Agriculture Sector

Machine Learning Technique for Crop Recommendation in Agriculture Sector

The Design of Hybrid Crop Recommendation System using Machine Learning Algorithms

Crop Recommendation using Machine Learning Techniques

Contact Info

Product

Resources

About